Harendra Singh
Papers
6
Total Citations
83
H-Index
4
About
Harendra Singh is a robotics researcher whose work focuses on intelligent control systems for robot manipulators, particularly in the presence of uncertainties. His core contributions lie at the intersection of neural networks and robust adaptive control, addressing critical challenges in both joint-space and task-space tracking. Singh’s most influential work, a 2012 paper on stability analysis of a robust adaptive hybrid position/force controller (46 citations), demonstrates his ability to tackle complex, real-world control problems. A key achievement is his development of neural network-based compensators that eliminate the need for prior knowledge of uncertainty bounds—a significant practical advantage over traditional robust controllers. This innovation is detailed in his 2010 paper (7 citations) and further explored in his 2011 task-space control paper (4 citations). Beyond control theory, Singh has also contributed to perception and mapping, as seen in his 2018 work on constructing 3D maps of indoor environments using RGB-D cameras (5 citations). His research on redundant manipulators, including a 2017 virtual experimental analysis (2 citations), rounds out a career dedicated to making robots more adaptable, reliable, and autonomous in uncertain, unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 4Construction of a 3D Map of Indoor Environment5 citations · 2018
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